Grid Dynamics - Data Scientist - Generative AI

Job Title : Data Scientist GenAI & Azure

Experience : 7 to 10 years

Location : Hyderabad / Bangalore

Notice Period : Immediate to 20 Days

Role Overview :

We are seeking a highly skilled and hands-on Data Scientist with deep expertise in Generative AI (GenAI), Python development, and Microsoft Azure. The ideal candidate will have experience designing and deploying scalable AI/ML solutions using modern GenAI frameworks and orchestration tools. This is an exciting opportunity to work on cutting-edge enterprise-grade AI applications.

Key Responsibilities :

- Design, develop, and deploy AI-driven applications using Python, REST API frameworks like Flask or FastAPI.

- Build and integrate GenAI/LLM-based solutions using tools such as LangChain, LangGraph, or similar orchestration frameworks.

- Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompt tuning strategies including Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Few-Shot Learning.

- Work with vector databases like FAISS, Pinecone, or Azure Cognitive Search for semantic search and embeddings.

- Leverage embedding models (e.g., Sentence Transformers, CLIP, SIGLIP) for knowledge retrieval and classification.

- Optimize performance and scale AI solutions for high payload volumes, manage tokens, and handle long-form data inputs.

- Integrate and transform structured and semi-structured data formats using schema mapping techniques.

- Deploy and manage solutions on Microsoft Azure, using services like App Services, Azure Functions, Blob Storage, and Cosmos DB.

- Use Git and CI/CD pipelines (Azure DevOps or GitHub Actions) for version control and continuous deployment.

Must-Have Skills :

- Advanced Python programming (OOP, asynchronous programming)

- Experience with REST APIs using Flask or FastAPI

- Hands-on with Microsoft Azure cloud ecosystem

- Proficiency in GenAI/LLM orchestration (LangChain, LangGraph, etc.)

- Familiarity with RAG patterns, prompt tuning, and embedding techniques

- Strong knowledge of vector DBs and semantic search

- Experience in scaling GenAI applications

- Solid understanding of schema transformation for varied data formats

- Experience with Git, Azure DevOps, or GitHub Actions

Nice-to-Have (Preferred) :

- Proven experience deploying GenAI applications in production (enterprise environment)

- Familiarity with AgentOps or MLOps pipelines

- Exposure to VLLMs or lightweight open-source LLMs

- Experience with production support / hypercare for live AI systems